Our approaches to understand protein interactions across the cell focus on uncovering, modeling and quantitating the general principles governing the micro and macro universe. This has always been an important component of biological research, however recent advances in experimental techniques and the accumulation of unprecedented genome-scale experimental data produced by these novel technologies now allow for addressing fundamental questions on a large scale. These relate to molecular interactions, principles of bimolecular recognition, and mechanisms of signal propagation. The functioning of a cell requires a variety of intermolecular interactions including proteinprotein, proteinDNA, proteinRNA, hormones, peptides, small molecules, lipids and more. Biomolecules work together to provide specific functions and perturbations in intermolecular communication channels often lead to cellular malfunction and disease. A full understanding of the interactome requires an in-depth grasp of the biophysical principles underlying individual interactions as well as their organization in cellular networks. Phenomena can be described at different levels of abstraction. Computational and systems biology strive to model cellular processes by integrating and analyzing complex data from multiple experimental sources using interdisciplinary tools. As a result, both the causal relationships between the variables and the general features of the system can be discovered, which even without knowing the details of the underlying mechanisms allow for putting forth hypotheses and predicting the behavior of the systems in response to perturbation. And here lies the strength of in silico models which provide control and predictive power. At the same time, the complexity of individual elements and molecules can be addressed by the fields of molecular biophysics, physical biology and structural biology, which focus on the underlying physico-chemical principles and may explain the molecular mechanisms of cellular function. Within this framework, we have addressed the question of what is the mechanism through which transcription factors (TFs) assemble specifically along the enhancer DNA? The IFN-beta enhanceosome provides a good model system: it is small;its components'crystal structures are available;and there are biochemical and cellular data. In the IFN-beta enhanceosome, there are few protein-protein interactions even though consecutive DNA response elements (REs) overlap. Our molecular dynamics (MD) simulations on different motif combinations from the enhanceosome illustrate that cooperativity is achieved via unique organization of the REs: specific binding of one TF can enhance the binding of another TF to a neighboring RE and restrict others, through overlap of REs;the order of the REs can determine which complexes will form;and the alternation of consensus and non-consensus REs can regulate binding specificity by optimizing the interactions among partners. Our observations offer an explanation of how specificity and cooperativity can be attained despite the limited interactions between neighboring TFs on the enhancer DNA. To date, when addressing selective TF binding, attention has largely focused on RE sequences. Yet, the order of the REs on the DNA and the length of the spacers between them can be a key factor in specific combinatorial assembly of the TFs on the enhancer and thus in function. Our results emphasize cooperativity via RE binding sites organization. On another venue, sumoylation is the covalent attachment of small ubiquitin-like modifier (SUMO) to a target protein. Similar to other ubiquitin-like pathways, three enzyme types are involved that act in succession: an activating enzyme (E1), a conjugating enzyme (E2), and a ligase (E3). To date, unlike other ubiquitin-like mechanisms, sumoylation of the target RanGAP1 (Target(RanGAP1)) does not absolutely require the E3 of the system, RanBP2 (E3(RanBP2)), since the presence of E2 (E2(Ubc9)) is enough to sumoylate Target(RanGAP1). However, in the presence of E3, sumoylation is more efficient. To understand the role of the target specificity of E3(RanBP2) and E2(Ubc9), we carried out molecular dynamics simulations for the structure of E2(Ubc9)-SUMO-Target(RanGAP1) with and without the E3(RanBP2) ligase. Analysis of the dynamics of E2(Ubc9)-SUMO-Target(RanGAP1) in the absence and presence of E3(RanBP2) revealed that two different allosteric sites regulate the ligase activity: (i) in the presence of E3(RanBP2), the E2(Ubc9)'s loop 2;(ii) in the absence of E3(RanBP2), the Leu65-Arg70 region of SUMO. These results provide a first insight into the question of how E3(RanBP2) can act as an intrinsic E3 for E2(Ubc9) and why, in its absence, the activity of E2(Ubc9)-SUMO-Target(RanGAP1) could still be maintained, albeit at lower efficiency. Proteins can exist in a large number of conformations around their native states that can be characterized by an energy landscape. The landscape illustrates individual valleys, which are the conformational substates. From the functional standpoint, there are two key points: first, all functionally relevant substates pre-exist;and second, the landscape is dynamic and the relative populations of the substates will change following allosteric events. Allosteric events perturb the structure, and the energetic strain propagates and shifts the population. This can lead to changes in the shapes and properties of target binding sites. We presented an overview of dynamic conformational ensembles focusing on allosteric events in signaling, and proposed that combining equilibrium fluctuation concepts with genomic screens could help drug discovery.